State Street has patented a technique for accurate financial portfolio evaluation using an approximate computing engine. The system uses machine learning to approximate Net Asset Value in real-time based on current data, determining precision for business decisions. GlobalData’s report on State Street gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on State Street, Grid computing was a key innovation area identified from patents. State Street's grant share as of January 2024 was 73%. Grant share is based on the ratio of number of grants to total number of patents.
Approximate net asset value calculation for financial portfolios
A recently granted patent (Publication Number: US11875408B2) discloses an innovative apparatus and method for approximating the Net Asset Value of a financial portfolio in real-time or near real-time using machine learning models. The apparatus includes memory, a processing circuit, and at least one hardware circuit to train the model on historical data, determine weights for data items, receive requests for Net Asset Value with timing attributes, input data items for a subset of funds, and output the approximation to trigger buy or sell transactions. The precision of the approximation is based on the subset size captured within a time limit, enhancing the efficiency of financial decision-making processes.
Furthermore, the patent details the operation of a private cloud service by the processing circuit, the storage of information items in an immutable log, and the calculation of derived values from these items. The system ensures the accuracy and reliability of the approximated Net Asset Value by storing information in distinct logs and refraining from altering data in the immutable log. The method involves training the machine learning model on historical data, processing current data items for a subset of funds within a time limit, determining the Net Asset Value approximation based on weights assigned to data items, and outputting the result to facilitate automated trading decisions. By incorporating timing attributes and precision considerations, the system offers a sophisticated approach to managing financial portfolios in a dynamic market environment, showcasing advancements in technology for real-time financial analysis and decision-making.
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